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Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
- Publication Year :
- 2020
-
Abstract
- The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.<br />Comment: Accepted at the ICML 2020 Workshop Graph Representation Learning and Beyond (GRL+)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2007.05292
- Document Type :
- Working Paper